Visualization, benchmarking and characterization of nested single-cell heterogeneity as dynamic forest mixtures.

Area of Science:

• Developmental Biology
• Computational Biology
• Genomics

Background:

• Debate exists on whether biological development is continuous or discontinuous.
• Existing pseudo-time models struggle with real-time, heterogeneous systems and temporal benchmarking.
• Temporal single-cell data presents challenges for accurately modeling dynamic biological processes.

Purpose of the Study:

• To develop a data-driven framework for analyzing temporal single-cell data.
• To address limitations of existing models in capturing complex, heterogeneous developmental trajectories.
• To provide a robust method for visualizing and characterizing dynamic biological processes.

Main Methods:

• Developed dynamic spanning forest mixtures (DSFMix), a framework using dependent minimum spanning trees.
• Employed decision-tree models for gene selection based on multimodality, skewness, and time.
• Utilized tree agglomerative hierarchical clustering and dynamic branch cutting to build gene networks.
• Benchmarked DSFMix against pseudo-time and temporal approaches for feature selection, time correlation, and network similarity.

Main Results:

• DSFMix effectively visualizes and characterizes complex biological processes, including epithelial-mesenchymal transition and immune response.
• Gene expression profiles during development are often non-uniformly distributed, right-skewed, and multimodal, indicating developmental steady states.
• Identified and validated gene signatures crucial for somatic and germline differentiation dynamics.

Conclusions:

• DSFMix offers a powerful approach for analyzing temporal single-cell data, overcoming limitations of existing methods.
• The framework enhances understanding of developmental trajectories and gene regulation.
• This study provides insights into the complex, dynamic nature of biological development and differentiation.